Model Base (v0.4.4)

We constantly work to expand our list of modules. Here is the list of feature extractors, ML models, objectives and optimizers implemented with the latest Modulos AutoML release.

Feature Extractors

  • Autoencoder
  • Cropping
  • Identity
  • Image Identity and Integer Encoding
  • Principal Component Analysis
  • Random Feature Selection
  • Random Pixel Selection
  • T-test Feature Selection
  • Table Preparation with One Hot Encoding
  • Table Preparation with Integer Encoding
  • Table Preparation with Standard Scaling

Models

  • Convolutional Neural Network (Keras)
  • Convolutional Neural Network incl. Metadata
  • Convolutional Neural Network (Pytorch)
  • LightGBM
  • Neural Architecture Search – Network Morphism
  • Neural Network
  • Random Forest
  • Ridge Regression
  • Support Vector Machine
  • XGBoost

Objectives

  • Accuracy
  • F1 Score (binary)
  • F1 Score (macro)
  • Mean Absolute Error (MAE)
  • Mean Absolute Percentage Error (MAPE)
  • Mean Absolute Scaled Error (MASE)
  • Median Absolute Error/Deviation (MAD)
  • Precision Score (macro)
  • R2 Score
  • Recall Score (macro)
  • Root Mean Squared Error

Optimizers

  • Bayesian Optimizer
  • Random Search Optimizer